#region .NET Disclaimer/Info

//===============================================================================
//
// gOODiDEA, uland.com
//===============================================================================
//
// $Header :		$  
// $Author :		$
// $Date   :		$
// $Revision:		$
// $History:		$  
//  
//===============================================================================

#endregion

#region Java

/* NeuQuant Neural-Net Quantization Algorithm
 * ------------------------------------------
 *
 * Copyright (c) 1994 Anthony Dekker
 *
 * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
 * See "Kohonen neural networks for optimal colour quantization"
 * in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
 * for a discussion of the algorithm.
 *
 * Any party obtaining a copy of these files from the author, directly or
 * indirectly, is granted, free of charge, a full and unrestricted irrevocable,
 * world-wide, paid up, royalty-free, nonexclusive right and license to deal
 * in this software and documentation files (the "Software"), including without
 * limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
 * and/or sell copies of the Software, and to permit persons who receive
 * copies from any such party to do so, with the only requirement being
 * that this copyright notice remain intact.
 */

// Ported to Java 12/00 K Weiner

#endregion

using System;

namespace ExtractorSharp.Core.Coder.Gif {
    public class NeuQuant {
        protected static readonly int netsize = 256; /* number of colours used */

        /* four primes near 500 - assume no image has a length so large */
        /* that it is divisible by all four primes */
        protected static readonly int prime1 = 499;
        protected static readonly int prime2 = 491;
        protected static readonly int prime3 = 487;
        protected static readonly int prime4 = 503;

        protected static readonly int minpicturebytes = 3 * prime4;
        /* minimum size for input image */
        /* Program Skeleton
           ----------------
           [select samplefac in range 1..30]
           [read image from input file]
           pic = (unsigned char*) malloc(3*width*height);
           initnet(pic,3*width*height,samplefac);
           learn();
           unbiasnet();
           [write output image header, using writecolourmap(f)]
           inxbuild();
           write output image using inxsearch(b,g,r)      */

        /* Network Definitions
           ------------------- */
        protected static readonly int maxnetpos = netsize - 1;
        protected static readonly int netbiasshift = 4; /* bias for colour values */
        protected static readonly int ncycles = 100; /* no. of learning cycles */

        /* defs for freq and bias */
        protected static readonly int intbiasshift = 16; /* bias for fractions */
        protected static readonly int intbias = 1 << intbiasshift;
        protected static readonly int gammashift = 10; /* gamma = 1024 */
        protected static readonly int gamma = 1 << gammashift;
        protected static readonly int betashift = 10;
        protected static readonly int beta = intbias >> betashift; /* beta = 1/1024 */

        protected static readonly int betagamma =
            intbias << (gammashift - betashift);

        /* defs for decreasing radius factor */
        protected static readonly int initrad = netsize >> 3; /* for 256 cols, radius starts */
        protected static readonly int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
        protected static readonly int radiusbias = 1 << radiusbiasshift;
        protected static readonly int initradius = initrad * radiusbias; /* and decreases by a */
        protected static readonly int radiusdec = 30; /* factor of 1/30 each cycle */

        /* defs for decreasing alpha factor */
        protected static readonly int alphabiasshift = 10; /* alpha starts at 1.0 */
        protected static readonly int initalpha = 1 << alphabiasshift;

        /* radbias and alpharadbias used for radpower calculation */
        protected static readonly int radbiasshift = 8;
        protected static readonly int radbias = 1 << radbiasshift;
        protected static readonly int alpharadbshift = alphabiasshift + radbiasshift;
        protected static readonly int alpharadbias = 1 << alpharadbshift;

        protected int alphadec; /* biased by 10 bits */
        /* for network lookup - really 256 */

        protected int[] bias = new int[netsize];

        /* bias and freq arrays for learning */
        protected int[] freq = new int[netsize];
        protected int lengthcount; /* lengthcount = H*W*3 */

        protected int[] netindex = new int[256];

        //   typedef int pixel[4];                /* BGRc */
        protected int[][] network; /* the network itself - [netsize][4] */
        protected int[] radpower = new int[initrad];

        protected int samplefac; /* sampling factor 1..30 */

        /* Types and Global Variables
        -------------------------- */

        protected byte[] thepicture; /* the input image itself */
        /* radpower for precomputation */

        /* Initialise network in range (0,0,0) to (255,255,255) and set parameters
           ----------------------------------------------------------------------- */
        public NeuQuant(byte[] thepic, int len, int sample) {
            int i;
            int[] p;

            thepicture = thepic;
            lengthcount = len;
            samplefac = sample;

            network = new int[netsize][];
            for (i = 0; i < netsize; i++) {
                network[i] = new int[4];
                p = network[i];
                p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
                freq[i] = intbias / netsize; /* 1/netsize */
                bias[i] = 0;
            }
        }

        public byte[] ColorMap() {
            var map = new byte[3 * netsize];
            var index = new int[netsize];
            for (var i = 0; i < netsize; i++) {
                index[network[i][3]] = i;
            }
            var k = 0;
            for (var i = 0; i < netsize; i++) {
                var j = index[i];
                map[k++] = (byte) network[j][0];
                map[k++] = (byte) network[j][1];
                map[k++] = (byte) network[j][2];
            }
            return map;
        }

        /* Insertion sort of network and building of netindex[0..255] (to do after unbias)
           ------------------------------------------------------------------------------- */
        public void Inxbuild() {
            int i, j, smallpos, smallval;
            int[] p;
            int[] q;
            int previouscol, startpos;

            previouscol = 0;
            startpos = 0;
            for (i = 0; i < netsize; i++) {
                p = network[i];
                smallpos = i;
                smallval = p[1]; /* index on g */
                /* find smallest in i..netsize-1 */
                for (j = i + 1; j < netsize; j++) {
                    q = network[j];
                    if (q[1] < smallval) {
                        /* index on g */
                        smallpos = j;
                        smallval = q[1]; /* index on g */
                    }
                }
                q = network[smallpos];
                /* swap p (i) and q (smallpos) entries */
                if (i != smallpos) {
                    j = q[0];
                    q[0] = p[0];
                    p[0] = j;
                    j = q[1];
                    q[1] = p[1];
                    p[1] = j;
                    j = q[2];
                    q[2] = p[2];
                    p[2] = j;
                    j = q[3];
                    q[3] = p[3];
                    p[3] = j;
                }
                /* smallval entry is now in position i */
                if (smallval != previouscol) {
                    netindex[previouscol] = (startpos + i) >> 1;
                    for (j = previouscol + 1; j < smallval; j++) {
                        netindex[j] = i;
                    }
                    previouscol = smallval;
                    startpos = i;
                }
            }
            netindex[previouscol] = (startpos + maxnetpos) >> 1;
            for (j = previouscol + 1; j < 256; j++) {
                netindex[j] = maxnetpos; /* really 256 */
            }
        }

        /* Main Learning Loop
           ------------------ */
        public void Learn() {
            int i, j, b, g, r;
            int radius, rad, alpha, step, delta, samplepixels;
            byte[] p;
            int pix, lim;

            if (lengthcount < minpicturebytes) {
                samplefac = 1;
            }
            alphadec = 30 + (samplefac - 1) / 3;
            p = thepicture;
            pix = 0;
            lim = lengthcount;
            samplepixels = lengthcount / (3 * samplefac);
            delta = samplepixels / ncycles;
            alpha = initalpha;
            radius = initradius;

            rad = radius >> radiusbiasshift;
            if (rad <= 1) {
                rad = 0;
            }
            for (i = 0; i < rad; i++) {
                radpower[i] =
                    alpha * ((rad * rad - i * i) * radbias / (rad * rad));
            }

            //fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);

            if (lengthcount < minpicturebytes) {
                step = 3;
            } else if (lengthcount % prime1 != 0) {
                step = 3 * prime1;
            } else {
                if (lengthcount % prime2 != 0) {
                    step = 3 * prime2;
                } else {
                    if (lengthcount % prime3 != 0) {
                        step = 3 * prime3;
                    } else {
                        step = 3 * prime4;
                    }
                }
            }

            i = 0;
            while (i < samplepixels) {
                b = (p[pix + 0] & 0xff) << netbiasshift;
                g = (p[pix + 1] & 0xff) << netbiasshift;
                r = (p[pix + 2] & 0xff) << netbiasshift;
                j = Contest(b, g, r);

                Altersingle(alpha, j, b, g, r);
                if (rad != 0) {
                    Alterneigh(rad, j, b, g, r); /* alter neighbours */
                }

                pix += step;
                if (pix >= lim) {
                    pix -= lengthcount;
                }

                i++;
                if (delta == 0) {
                    delta = 1;
                }
                if (i % delta == 0) {
                    alpha -= alpha / alphadec;
                    radius -= radius / radiusdec;
                    rad = radius >> radiusbiasshift;
                    if (rad <= 1) {
                        rad = 0;
                    }
                    for (j = 0; j < rad; j++) {
                        radpower[j] =
                            alpha * ((rad * rad - j * j) * radbias / (rad * rad));
                    }
                }
            }
            //fprintf(stderr,"finished 1D learning: readonly alpha=%f !\n",((float)alpha)/initalpha);
        }

        /* Search for BGR values 0..255 (after net is unbiased) and return colour index
           ---------------------------------------------------------------------------- */
        public int Map(int b, int g, int r) {
            int i, j, dist, a, bestd;
            int[] p;
            int best;

            bestd = 1000; /* biggest possible dist is 256*3 */
            best = -1;
            i = netindex[g]; /* index on g */
            j = i - 1; /* start at netindex[g] and work outwards */

            while (i < netsize || j >= 0) {
                if (i < netsize) {
                    p = network[i];
                    dist = p[1] - g; /* inx key */
                    if (dist >= bestd) {
                        i = netsize; /* stop iter */
                    } else {
                        i++;
                        if (dist < 0) {
                            dist = -dist;
                        }
                        a = p[0] - b;
                        if (a < 0) {
                            a = -a;
                        }
                        dist += a;
                        if (dist < bestd) {
                            a = p[2] - r;
                            if (a < 0) {
                                a = -a;
                            }
                            dist += a;
                            if (dist < bestd) {
                                bestd = dist;
                                best = p[3];
                            }
                        }
                    }
                }
                if (j >= 0) {
                    p = network[j];
                    dist = g - p[1]; /* inx key - reverse dif */
                    if (dist >= bestd) {
                        j = -1; /* stop iter */
                    } else {
                        j--;
                        if (dist < 0) {
                            dist = -dist;
                        }
                        a = p[0] - b;
                        if (a < 0) {
                            a = -a;
                        }
                        dist += a;
                        if (dist < bestd) {
                            a = p[2] - r;
                            if (a < 0) {
                                a = -a;
                            }
                            dist += a;
                            if (dist < bestd) {
                                bestd = dist;
                                best = p[3];
                            }
                        }
                    }
                }
            }
            return best;
        }

        public byte[] Process() {
            Learn();
            Unbiasnet();
            Inxbuild();
            return ColorMap();
        }

        /* Unbias network to give byte values 0..255 and record position i to prepare for sort
           ----------------------------------------------------------------------------------- */
        public void Unbiasnet() {
            int i;

            for (i = 0; i < netsize; i++) {
                network[i][0] >>= netbiasshift;
                network[i][1] >>= netbiasshift;
                network[i][2] >>= netbiasshift;
                network[i][3] = i; /* record colour no */
            }
        }

        /* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
           --------------------------------------------------------------------------------- */
        protected void Alterneigh(int rad, int i, int b, int g, int r) {
            int j, k, lo, hi, a, m;
            int[] p;

            lo = i - rad;
            if (lo < -1) {
                lo = -1;
            }
            hi = i + rad;
            if (hi > netsize) {
                hi = netsize;
            }

            j = i + 1;
            k = i - 1;
            m = 1;
            while (j < hi || k > lo) {
                a = radpower[m++];
                if (j < hi) {
                    p = network[j++];
                    try {
                        p[0] -= a * (p[0] - b) / alpharadbias;
                        p[1] -= a * (p[1] - g) / alpharadbias;
                        p[2] -= a * (p[2] - r) / alpharadbias;
                    } catch (System.Exception) { } // prevents 1.3 miscompilation
                }
                if (k > lo) {
                    p = network[k--];
                    try {
                        p[0] -= a * (p[0] - b) / alpharadbias;
                        p[1] -= a * (p[1] - g) / alpharadbias;
                        p[2] -= a * (p[2] - r) / alpharadbias;
                    } catch (System.Exception) { }
                }
            }
        }

        /* Move neuron i towards biased (b,g,r) by factor alpha
           ---------------------------------------------------- */
        protected void Altersingle(int alpha, int i, int b, int g, int r) {
            /* alter hit neuron */
            var n = network[i];
            n[0] -= alpha * (n[0] - b) / initalpha;
            n[1] -= alpha * (n[1] - g) / initalpha;
            n[2] -= alpha * (n[2] - r) / initalpha;
        }

        /* Search for biased BGR values
           ---------------------------- */
        protected int Contest(int b, int g, int r) {
            /* finds closest neuron (min dist) and updates freq */
            /* finds best neuron (min dist-bias) and returns position */
            /* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
            /* bias[i] = gamma*((1/netsize)-freq[i]) */

            int i, dist, a, biasdist, betafreq;
            int bestpos, bestbiaspos, bestd, bestbiasd;
            int[] n;

            bestd = ~(1 << 31);
            bestbiasd = bestd;
            bestpos = -1;
            bestbiaspos = bestpos;

            for (i = 0; i < netsize; i++) {
                n = network[i];
                dist = n[0] - b;
                if (dist < 0) {
                    dist = -dist;
                }
                a = n[1] - g;
                if (a < 0) {
                    a = -a;
                }
                dist += a;
                a = n[2] - r;
                if (a < 0) {
                    a = -a;
                }
                dist += a;
                if (dist < bestd) {
                    bestd = dist;
                    bestpos = i;
                }
                biasdist = dist - (bias[i] >> (intbiasshift - netbiasshift));
                if (biasdist < bestbiasd) {
                    bestbiasd = biasdist;
                    bestbiaspos = i;
                }
                betafreq = freq[i] >> betashift;
                freq[i] -= betafreq;
                bias[i] += betafreq << gammashift;
            }
            freq[bestpos] += beta;
            bias[bestpos] -= betagamma;
            return bestbiaspos;
        }
    }
}